The "LLM Agents MOOC (Massively Open Online Course)" offered by the Berkeley Center for Responsible Distributed Intelligence (Berkeley RDI) recently explored a cutting-edge area of AI - Large Language Models as agents. The course was designed for learners interested in the intersection of LLMs and agentic behavior, encompassing the historical evolution, modern applications, and future potential of this technology, and featured weekly guest lecturers from industry and academia. We found it to be a great experience, and even better, it was provided by Berkeley RDI free of charge.
A big shout-out and thanks to UC Berkeley Professor Dawn Song and her team for creating the MOOC.
You can find more details at: LLM Agents MOOC Syllabus
And all of the lectures are on their YouTube channel here: LLM Agents MOOC Videos
These are the topics covered throughout the twelve-week course:
Week 1: LLM Reasoning: The course started with a lecture exploring Large Language Model reasoning capabilities. While LLMs which were initially trained to predict the next word in a sequence struggle with tasks requiring reasoning, incorporating intermediate steps dramatically improves their performance. This is achieved through various methods: training with intermediate steps, fine-tuning on datasets with rationale, and prompting with explicit instructions to show work. The lecture investigates the impact of reasoning strategies, like least-to-most prompting and analogical reasoning, and addresses limitations such as susceptibility to irrelevant context and the inability to self-correct. Ultimately, the lecture advocates for a deeper understanding of LLM reasoning mechanisms to unlock their full potential, suggesting a shift toward problem definition as crucial for future advancements.
Week 2: LLM Agents Brief History and Overview: The second week's presentation discusses the history of LLM agents, tracing their evolution from early rule-based systems like ELIZA to sophisticated reasoning agents like ReAct. The lecture emphasizes the key components of LLM agents: action, observation, and reasoning, highlighting the transition from domain-specific, manually designed agents to more general, few-shot learning models enabled by LLMs. The lecture explores future directions, focusing on challenges like long-term memory, robust human-computer interfaces, and the development of more comprehensive agent benchmarks.
Week 3: Agentic AI Frameworks & AutoGen, Building a Multimodal Knowledge Assistant: The first part of the lecture details AutoGen's architecture, design patterns, and successful applications, highlighting its growing popularity and impact on the field of AI development. The framework facilitates multi-agent orchestration, allowing developers to easily combine different AI models, tools, and human input to build sophisticated applications. The second section of the lecture gave an overview of Llamaindex. Key aspects highlighted included LlamaParse (an advanced document parser), LlamaCloud (a production-ready RAG platform), and the use of agent orchestration workflows for managing complex interactions and achieving scalability.
Week 4: Enterprise Trends for Generative AI, and Key Components of Building Successful Agents/Applications: This lecture highlights the rapid advancements in AI, driven by increased scale in computation, data, and model size, showcasing examples like Google's Gemini multimodal model. Key trends discussed include the accelerating development pace, the shift towards general-purpose models, the importance of platform choice, and the decreasing cost of API calls. The lecture also emphasizes the need for integrating LLMs with search functionalities and explores crucial factors for successful generative AI deployment, such as access to diverse models, model management platforms, and customization capabilities. Finally, it delves into challenges like hallucinations and outdated information, proposing solutions like function calling to enhance LLM reliability and integrate them with external resources.
Week 5: Compound AI Systems & the DSPy Framework: Week 5 introduced the DSPy framework, a powerful tool for building and optimizing grounded LLM programs. DSPy leverages bootstrapped demonstrations, dataset and program summaries, and reflexive views of the LM program code to guide the generation of new instructions. The presentation included practical methods like Bootstrap Few-shot and MIPRO, exploring the co-optimization of instructions and few-shot examples.
Week 6: Agents for Software Development: This presentation by Graham Neubig, a CMU professor and Chief Scientist at All Hands AI, explores the burgeoning field of software development agents. It highlights the increasing importance of software in various industries and the consequent need for more efficient development tools. The presentation details the challenges in building such agents, focusing on issues like defining appropriate action/observation spaces, code generation, file localization, and robust planning and error recovery.
Week 7: AI Agents for Enterprise Workflows: This week examined agent workflows and applications with examples using ServiceNow's WorkArena, BrowserGym, and AgentLab. These platforms were shown to be helpful in evaluating and comparing different agents' capabilities in realistic, web-based settings.
Week 8: Towards a Unified Framework of Neural and Symbolic Decision Making: This lecture explores methods for improving Large Language Model performance in complex planning tasks, including travel planning. The presentation investigates three main approaches: scaling up LLMs (which is expensive), creating hybrid systems that combine LLMs with combinatorial solvers for optimal solutions using techniques like Mixed Integer Linear Programming (MILP), and Searchformer (a transformer-based search).
Week 9: Project GR00T: A Blueprint for Generalist Robotics: This lecture covered Project GR00T, NVIDIA's initiative to develop a generalist humanoid robot, leveraging three core principles: a data pyramid for training, a foundation agent for control, and embodiment for real-world interaction. The project utilizes a generative simulation framework to create diverse training data, combining techniques like reinforcement and imitation learning along with large language models for task planning and code generation.
Week 10: Open-Source and Science in the Era of Foundation Models: Week 10 provided an overview of the variations on "open-source" present with LLMs today. The lecture also included discussions on the ethical implications and future directions of agentic LLM research, emphasizing the importance of responsible AI development and deployment.
Week 11: Measuring Agent Capabilities and Anthropic’s RSP: In this lecture Ben Mann from Anthropic discusses their Responsible Scaling Policy (RSP), which outlines a framework for the ethical development and deployment of large language models. The RSP emphasizes the importance of aligning AI systems with human values, ensuring safety, and promoting transparency throughout the scaling process.
Week 12: LLM Agent Safety: In the final week of the course, UC Berkeley Professor and LLM Agents MOOC co-instructor, Dawn Song, provided an overview of the state of the art with respect to LLM agent safety.
The MOOC provided a comprehensive learning experience on LLM agents, complete with quizzes, labs, and a hackathon. Participants could pick from several tiers for certificates of completion. We found it immensely valuable and hope Berkeley RDI continues to provide these sorts of courses and events.
Check out the mind map we created below, which visually organizes some of the core concepts from the course!
